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Research & Innovations at Car Lab
1. CarLabOverview(drinking from a fire hydrant) Miklos A. Vasarhelyi Director CarLab KPMG Professor of AIS 8th Annual CarLab Advisory Board Meeting November 4th, 2010
18. 5 The CarLab Research Efforts Siemens Continuous control monitoring (CCM) Audit automation Authorization and control structure project Continuity equations at HCA Itau Unibanco Branch monitoring and analytics Transitory Accounts Product sales monitoring Insurance company Forensics as CA -> the wires project Claims 5
19. P&G KPI project Order to cash project -> selective automation Vendor / payments project KPMG projects Technology adoption at CPA firms Technology adoption at IA departments The future of audit Rapid Prototyping Environment for data research (RPE) Dashboard / visualization / Advanced Analytics Representation (DVAA) Telecom and Real Estate Co (TRE) Duplicate Payments detection The CarLab Research Efforts (2) 6
20. RARC and Our Teaching Mission The VPA effort Automatic course recording experiment just started A vision of free unencumbered educational stream for less developed nations Clickers Experimentation with response system uses in different contexts 7
21. Out of the box Standards Formalization: issuing standards in autocode 8
22. AAA Impact of Research Taskforce 2009 Perhaps the most important contribution of accounting information systems research to practice in the auditing and assurance domain is in continuous assurance. The work of Vasarhelyi and his colleagues on continuous assurance demonstrates the application of strong theoretical foundations to the practical problems of the auditor; in this case the internal auditor. 9
27. CA System Data Segments for Analytical Modeling: 1,2,3,4,5,6……100 CA System Data Segments for Analytical Modeling: 1,2,3,4,5,6……100, 101 CA System Data Segments for Analytical Modeling: 1,2,3,4,5,6……100, 101, 102 101 Predicted Value 101 102 103 AP Model 1 102 103 104 102 Predicted Value AP Model 2 103 104 105 103 Predicted Value AP Model 3 updated updated 14
28. Initial Model Estimation Determine Parameter p-value threshold Retain parameters below threshold; Restrict parameters over threshold to zero Re-estimate Model No Do new parameter estimates all below threshold? Yes Final Model 15
29. CA System Data Segments for Analytical Modeling: 1,2,3,4,5,6……100 101 Predicted Value 101 102 103 AP Model 1 16
32. The Role of Documentation Within an Organization 19
33. Risk Monitoring Workflow File Setup Gather basic process, internal audit team info Information from External Interfaces such as ….. Risk-Factor Assessments Evaluate ERP for flagged data, controls Administrative Functions Update data, risk factors, flag data Risk Scoring Mathematical scoring algorithm is applied and presented in a dashboard Begin automatic data collection, assign remote and in loco auditors YES On-Demand Audit Decision Auditor decides whether an in-depth audit is required Assign low priority to process until risk change NO 20
35. 22 22 Opportunities for Research Creating Control system measurement and monitoring schemata Creating standards for Business Process Monitoring and Alarming Automatic Confirmation Tools Development of a variety of modular Audit bots (agents) to be incorporated into programs of audit automation Creation of alternative real-time audit reports for different compliance masters
36. 23 Complementary Research Needs Expansion of assurance to non-financial processes to relate to these through continuity equations (Kogan et al, 2010) Standards are needed for CA (CICA/ AICPA, 1999; IIA, 2003; ISACA 2010) Research on the development of complementary assurance products
37. 24 Reconsideration of Concepts and Standards Independence needs to be re-defined The external audit billing model has to be restructured to bill on function not hours Audit firms must put improved knowledge collection and management processes to feed their audit analytic toolkit Audit firms have to engage in auditor automation and pro-actively promote corporate data collection during-the-process Value added must be justified in terms of data quality Materiality needs to be redefined
42. What is Process Mining of Event Logs? The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Fuelled by the omnipresence of event logs in transactional information systems… process mining has become a vivid research area. http://is.tm.tue.nl/staff/wvdaalst/BPMcenter/process%20mining.htm 29 29
44. Research team (all in AIS department but multidisciplinary background) Miklos A. Vasarhelyi, Prof. II (continuous auditing) Alex Kogan, Prof. – (computer scientist) Michael Alles, Professor –( economics) Helen Brown (auditing) Don Warren, Professor –( audit Practice) Trevor Stewart, Professor (retired D&T partner) Silvia Romero – faculty Montclair University Yong Bum Kim – PhD Student (Statistics) Daewoon Moon –PhD Student (Risk Management) Ryan Teter – PhD Student (Computer Science) Qi, Liu–PhD Student Hassan Issa–PhD Student David Chen–PhD Student (knowledge engineering) Danielle Lombardi (PhD student) JP Krahel (PhD student) Karina Chandia (PhD Student) 31
45. http://raw.rutgers.edu A wide range of presentations / videos and papers from the multiple CA and CR conferences promoted by Rutgers 32
47. RPE Overview Client Challenge: External audit work and audit-related advisory services progressively incorporate advanced analytics and knowledge bases. Area of Research: Evolved application of analytic methods Potential Use: Building on CAR-Lab experience from client projects(general ledger, transitory accounts, claims, wires, audit automation, loans, credit, with representative datasets for experimentation and demonstration) and employing existing public domain packages (such as WEKA or D), perform the following: Create masked datasets for demonstration of different techniques. Focus on technologies to deal with real-time and archival data feeds, in particular screening and automatic pattern recognition. A wide set of management problems falls into this domain which is being changed by the constant updating of data. The software tools, data, and models available will allow for testing the adequacy and demonstration of capabilities of advanced analytics. Example of Output: Use continuity equations, automatic error correction, delayed time contingent equations, and other linear methods to represent HCA transaction flow *See CAR-Lab-related qualifications and experiences on following pages
56. DVAA Overview Client Challenge: Develop dashboards for risk monitoring / develop other visualizationprototypes for continuous assurance (CA), continuous monitoring (CM), continuous risk measurement and assessment (CRMA), and continuous controls monitoring (CCM) Area of Research: Risk and transaction monitoring and human-computer interaction Potential Use: Creation of KPI- and KRI-based views of the world in a continuous audit environment including status-quo, projection, and interpretations Management routines / internal audit response to continuous assurance alerts / alarm events KPIs and KRIs for selected major business processes and/or regulated industries such as financial services or energy (trading businesses)
57. DVAA Overview (2) Examples of Output: Tied to the management of unobservable controls at a large ERP in an industrial setting -> special artifacts for the representation of different controls, alarms for control deficiencies, indices of control coverage, other graphics action objects Aimed at controlling the different dimensions of risk measurement encompassing direct links to quantitative models as well as arbitrary qualitative items Tied to the management of large volumes of data in the understanding, management and transaction investigation in a transitory account setting within a financial institution
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61. With ERM companies should identify and manage all risks to achieving its objectives.
62. In compliance with Basel, banks are required to assess their overall capital adequacy in relation to their risk profile.
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64. 43 Key Risk Indicator (KRI) KRI provides early warning systems to track the level of risk in the organization KRI can be identified through analysis of key business activities 6 steps for KRI identification (Scandizzo, 2005) Well identified and computed KRIs provide a reliable basis for computing the riskiness of firm for specific risk, such operational risk, liquidity risk, as well as the overall riskiness of firm. f(KRI(i),KRI(ii),…KRI(n))= Risk exposure External risk factors may be mapped manually into the computation of KRIs and risk exposure.
65. 44 CDA, CCM, and CRMA Within CRMA, key business processes and risk factors are identified to compute KRIs and risk exposures, and they are continuously monitored. CCM monitors violation against business controls and provides assurance on whether controls are followed. This mitigates control KRIs. CRMA continuously monitors changes in control KRIs with CCM, assuring CCM is working. CDA validates transaction data and evaluates quality of transactions. This mitigates data management KRIs. CRMA assures CDA is effective by keep monitoring. CDA also helps feeding CRMA with validated transaction data.
66. 45 CRMA Architecture CRMA KRI CDA CMM KRI f(KRIs) = Risk exposure Enterprise System KRI R&D Process 1 Accounting HR Marketing Process 2 Process 3 Warehouse Purchasing
